Project Part 2

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This notebook contains the code samples found in Chapter 3, Section 5 of Deep Learning with R. Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.

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Data Exploration & Preparation

• Our goal in the second part of the assignment is to predict how good a (new) customer will pay back their credit card depts. In the data set application data from current customers (the first 18 attributes) together with their status (last attribute; target) are given.
  
• The attributes from the applications are

Attribute Name	Explanation	Remarks
ID	Client	number
CODE_GENDER	Gender
FLAG_OWN_CAR	Is there a car
FLAG_OWN_REALTY	Is there a property
CNT_CHILDREN	Number of children
AMT_INCOME_TOTAL	Annual income
NAME_INCOME_TYPE	Income category
NAME_EDUCATION_TYPE	Education level
NAME_FAMILY_STATUS	Marital status
NAME_HOUSING_TYPE	Way of living
DAYS_BIRTH	Birthday	Count backwards from current day (0), -1 means yesterday
DAYS_EMPLOYED	Start date of employment	Count backwards from current day(0). If positive, it means the person unemployed.
FLAG_MOBIL	Is there a mobile phone
FLAG_WORK_PHONE	Is there a work phone
FLAG_PHONE	Is there a phone
FLAG_EMAIL	Is there an email
OCCUPATION_TYPE	Occupation
CNT_FAM_MEMBERS	Family size

• The last attribute status contains the “pay-back behavior”, i.e. when did that customer pay back their depts:
  • 0: 1-29 days past due
  • 1: 30-59 days past due
  • 2: 60-89 days overdue
  • 3: 90-119 days overdue
  • 4: 120-149 days overdue
  • 5: Overdue or bad debts, write-offs for more than 150 days
  • C: paid off that month
  • X: No loan for the month Please note: We are learning only the pay-back behavior. The decision, i.e. if we accept a customer or not, is done in another process step – not here!

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Main task

• Design your network. Why did you use a feed-forward network, or a convolutional or recursive network – and why not?
  
• Use k-fold validation (with k = 10) to find the best hyperparameters for your network.
• Use the average of the accuracy to evaluate the performance of your trained network.
• Find a “reasonable” good model. Argue why that model is reasonable. If you are not able to find a reasonable good model, explain what you all did to find a good model and argue why you think that’s not a good model.
  
• Save your trained neural network with save_model_hdf5. Also save your data sets you used for training, testing and validation.

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Some hints

• Data preprocessing is easier here; no feature engineering is needed.
• You may be able to reuse parts of the exercises we used in our examples during lectures.
• All in- and output values need to be floating numbers (or integers in exceptions) in the range of [0,1].
• Please note that a neural network expects a R matrix or vector, not data frames. Transform your data (e.g. a data frame) into a matrix with data.matrix if needed.
  
• There are some models which show an accuracy higher than 90% (!) for training (and test) data – after learning more than 1000 epochs.

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Important notes

• Single-label, Multiclass classification problem on page 73 in Deep Learning with R
• Spaces must be removed in between ‘{r}' and '’, else an error with ‘’ will be produced
• K-Fold Validation on page 83ff and 94ff in Deep Learning with R
• Page 110, use Last-Layer activation softmax and loss function categorical_crossentropy
• Convolutional network ausgeschlossen, weil hauptsächlich Pattern recognition/image classification
• Recursive ausgeschlossen, weil hauptsächlich für TimeSeries-Vorhersagen verwendet, oder für Vorhersagen
• Feed-Forward, weil Classification-Task

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Data import

#install.packages("tidymodels")
#install.packages("themis")
library(here)
library(tidyverse)
library(ggplot2)
library(dplyr)
library(tensorflow)
library(tfdatasets)
library(tidymodels)
library(keras)
library(caret)
library(themis)
#LOAD DATA
setwd(getwd())
dataIn = "../Data/Dataset-part-2.csv"
data_in <- read.csv(dataIn,header = TRUE, sep =',')
#View(data_in)
data <- data.frame(data_in)
summary(data)

       ID          CODE_GENDER        FLAG_OWN_CAR       FLAG_OWN_REALTY     CNT_CHILDREN     AMT_INCOME_TOTAL 
 Min.   :5008804   Length:67614       Length:67614       Length:67614       Min.   : 0.0000   Min.   :  26100  
 1st Qu.:5465941   Class :character   Class :character   Class :character   1st Qu.: 0.0000   1st Qu.: 112500  
 Median :5954270   Mode  :character   Mode  :character   Mode  :character   Median : 0.0000   Median : 157500  
 Mean   :5908133                                                            Mean   : 0.4206   Mean   : 178867  
 3rd Qu.:6289080                                                            3rd Qu.: 1.0000   3rd Qu.: 225000  
 Max.   :7965248                                                            Max.   :19.0000   Max.   :6750000  
 NAME_INCOME_TYPE   NAME_EDUCATION_TYPE NAME_FAMILY_STATUS NAME_HOUSING_TYPE    DAYS_BIRTH     DAYS_EMPLOYED   
 Length:67614       Length:67614        Length:67614       Length:67614       Min.   :-25201   Min.   :-17531  
 Class :character   Class :character    Class :character   Class :character   1st Qu.:-19438   1st Qu.: -2886  
 Mode  :character   Mode  :character    Mode  :character   Mode  :character   Median :-15592   Median : -1305  
                                                                              Mean   :-15914   Mean   : 62022  
                                                                              3rd Qu.:-12347   3rd Qu.:  -321  
                                                                              Max.   : -7489   Max.   :365243  
   FLAG_MOBIL FLAG_WORK_PHONE    FLAG_PHONE       FLAG_EMAIL     OCCUPATION_TYPE    CNT_FAM_MEMBERS 
 Min.   :1    Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Length:67614       Min.   : 1.000  
 1st Qu.:1    1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   Class :character   1st Qu.: 2.000  
 Median :1    Median :0.0000   Median :0.0000   Median :0.0000   Mode  :character   Median : 2.000  
 Mean   :1    Mean   :0.2028   Mean   :0.2742   Mean   :0.1005                      Mean   : 2.174  
 3rd Qu.:1    3rd Qu.:0.0000   3rd Qu.:1.0000   3rd Qu.:0.0000                      3rd Qu.: 3.000  
 Max.   :1    Max.   :1.0000   Max.   :1.0000   Max.   :1.0000                      Max.   :20.000  
    status         
 Length:67614      
 Class :character  
 Mode  :character  
                   
                   
                   

plot(data$status)

##Cleanup

# Check for duplicates 
sum(duplicated(data))

[1] 0

# No duplicates

#Remove ID (irrelevant) and FLAG_MOBIL (always 1)
data <- data %>% select(-ID, -FLAG_MOBIL)
cols <- c("CODE_GENDER","FLAG_OWN_CAR","FLAG_OWN_REALTY","NAME_INCOME_TYPE","NAME_EDUCATION_TYPE", "NAME_FAMILY_STATUS", "NAME_HOUSING_TYPE","FLAG_WORK_PHONE","FLAG_PHONE","FLAG_EMAIL", "OCCUPATION_TYPE","status")
cols

 [1] "CODE_GENDER"         "FLAG_OWN_CAR"        "FLAG_OWN_REALTY"     "NAME_INCOME_TYPE"   
 [5] "NAME_EDUCATION_TYPE" "NAME_FAMILY_STATUS"  "NAME_HOUSING_TYPE"   "FLAG_WORK_PHONE"    
 [9] "FLAG_PHONE"          "FLAG_EMAIL"          "OCCUPATION_TYPE"     "status"             

data[cols] <- lapply(data[cols],factor)

# Replacing empty values with "Unknown"
levels(data$OCCUPATION_TYPE) <- c(levels(data$OCCUPATION_TYPE), "Unknown")
data$OCCUPATION_TYPE[is.na(data$OCCUPATION_TYPE)] <- "Unknown"

# Replacing C and X in Status
levels(data$status)[levels(data$status)=="C"] <- "6"
#data$status[data$status == "X"] <- 7
levels(data$status)[levels(data$status)=="X"] <- "7"
# #Convert factors into numericals
# data %<>% mutate_if(is.factor, as.numeric)

summary(data)

 CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN     AMT_INCOME_TOTAL              NAME_INCOME_TYPE
 F:43924     N:43107      N:21090         Min.   : 0.0000   Min.   :  26100   Commercial associate:15640  
 M:23690     Y:24507      Y:46524         1st Qu.: 0.0000   1st Qu.: 112500   Pensioner           :11982  
                                          Median : 0.0000   Median : 157500   State servant       : 5044  
                                          Mean   : 0.4206   Mean   : 178867   Student             :    4  
                                          3rd Qu.: 1.0000   3rd Qu.: 225000   Working             :34944  
                                          Max.   :19.0000   Max.   :6750000                               
                                                                                                          
                    NAME_EDUCATION_TYPE            NAME_FAMILY_STATUS           NAME_HOUSING_TYPE
 Academic degree              :   38    Civil marriage      : 6016    Co-op apartment    :  227  
 Higher education             :16890    Married             :44906    House / apartment  :60307  
 Incomplete higher            : 2306    Separated           : 4125    Municipal apartment: 2303  
 Lower secondary              :  716    Single / not married: 9528    Office apartment   :  587  
 Secondary / secondary special:47664    Widow               : 3039    Rented apartment   : 1020  
                                                                      With parents       : 3170  
                                                                                                 
   DAYS_BIRTH     DAYS_EMPLOYED    FLAG_WORK_PHONE FLAG_PHONE FLAG_EMAIL    OCCUPATION_TYPE  CNT_FAM_MEMBERS 
 Min.   :-25201   Min.   :-17531   0:53904         0:49071    0:60819    Unknown    :20699   Min.   : 1.000  
 1st Qu.:-19438   1st Qu.: -2886   1:13710         1:18543    1: 6795    Laborers   :12425   1st Qu.: 2.000  
 Median :-15592   Median : -1305                                         Sales staff: 6899   Median : 2.000  
 Mean   :-15914   Mean   : 62022                                         Core staff : 6059   Mean   : 2.174  
 3rd Qu.:-12347   3rd Qu.:  -321                                         Managers   : 4950   3rd Qu.: 3.000  
 Max.   : -7489   Max.   :365243                                         Drivers    : 4427   Max.   :20.000  
                                                                         (Other)    :12155                   
     status     
 0      :52133  
 1      : 6491  
 7      : 5790  
 6      : 1805  
 2      :  712  
 5      :  374  
 (Other):  309  

Preprocessing

set.seed(1)
trainIndex <- initial_split(data, prop = 0.8, strata = status) 
trainingSet <- training(trainIndex)
testSet <- testing(trainIndex)
status_folds <- vfold_cv(trainingSet, v = 10, strata = status)
status_folds

#  10-fold cross-validation using stratification 

set.seed(5)
preprocRecipe <-
  recipe(status ~., data = data) %>%
  step_dummy(all_nominal(), -status,  one_hot = TRUE) %>%
  step_range(all_predictors(), -all_nominal(), min = 0, max = 1)%>%
 # step_downsample(status, over_ratio = 1) %>%
  step_smote(status, over_ratio = 0.5, skip=TRUE) %>%
 # step_smotenc(status, over_ratio = 1) %>%
 #step_adasyn(status, over_ratio = 1) %>%
 #step_nearmiss(status, over_ratio = 1) %>%
   
  step_dummy(status,  one_hot = TRUE)# %>%

In this step the above defined receipt is extracted using the prep() function, and then use the bake() function to transform a set of data based on that recipe.

# retain = TRUE and new_data = NULL ensures that pre-processed trainingSet is returned 
trainingSet_processed <- preprocRecipe %>%
  prep(trainingSet, retain = TRUE) %>%
  bake(new_data = NULL)
testSet_processed <- preprocRecipe %>%
  prep(testSet) %>%
  bake(new_data =testSet)

#summary(trainingSet_processed)

Check data

# summarize the class distribution
percentage <- prop.table(table(data$status)) * 100
cbind(freq=table(data$status), percentage=percentage)

   freq percentage
0 52133 77.1038542
1  6491  9.6000828
2   712  1.0530364
3   195  0.2884018
4   114  0.1686041
5   374  0.5531399
6  1805  2.6695655
7  5790  8.5633153

# Turn data frame into data matrix
matrix_data <- trainingSet_processed %>% select(-tail(names(trainingSet_processed), 8))
matrix_targets <- trainingSet_processed %>% select(tail(names(trainingSet_processed), 8))

matrix_data_test  <- testSet_processed %>% select(-tail(names(testSet_processed), 8))
matrix_targets_test  <- testSet_processed %>% select(tail(names(testSet_processed), 8))

#Subset only 100 entries for testing
#matrix_data <- matrix_data[1:100, ]
#matrix_targets <- matrix_targets[1:100, ]

Build Model

#train_data <- matrix_data
train_data <- data.matrix(matrix_data)
test_data <- data.matrix(matrix_data_test)
train_targets <- data.matrix(matrix_targets)
test_targets <- data.matrix(matrix_targets_test)

# Function to build the model
build_model <- function() {
  model <- keras_model_sequential() %>%
    #layer_batch_normalization(axis = -1L, input_shape = dim(train_data)[[2]]) %>%
    layer_dense(units = 64, activation = "relu", input_shape = dim(train_data)[[2]]) %>%
    layer_dense(units = 64, activation = "relu") %>%
    layer_dense(units = 64, activation = "relu") %>%
    layer_dense(units = 64, activation = "relu") %>%
    layer_dense(units = 8, activation = "softmax") 

  model %>% compile(
    optimizer = optimizer_sgd(learning_rate = 0.2),
    loss = "categorical_crossentropy",
    metrics = "categorical_accuracy"
  )

}

K-Fold-Validation

# mean <- apply(matrix_data, 2, mean)
# std <- apply(matrix_data, 2, sd)
# train_data <- scale(matrix_data, center = mean, scale = std)
# test_data <- scale(matrix_data, center = mean, scale = std)
# train_targets <- matrix_targets


k <- 3
indices <- sample(1:nrow(train_data))
folds <- cut(indices, breaks = k, labels = FALSE)

num_epochs <- 100
all_acc_histories <- NULL
for (i in 1:k) {
  cat("processing fold #", i, "\n")

  val_indices <- which(folds == i, arr.ind = TRUE)
  val_data <- train_data[val_indices,] #test_data#
  val_targets <- train_targets[val_indices,] #test_targets#
  
  partial_train_data <- train_data[-val_indices,]
  partial_train_targets <- train_targets[-val_indices,]
  model <- build_model()

  # Train the model (in silent mode, verbose=0)
  # Batch size https://stats.stackexchange.com/questions/153531/what-is-batch-size-in-neural-network
  # One epoch = one forward pass and one backward pass of all the training examples
  # Batch size = the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you'll need.
  # Number of iterations = number of passes, each pass using [batch size] number of examples. To be clear, one pass = one forward pass + one backward pass (we do not count the forward pass and backward pass as two different passes).
  # Batch size 32 much faster than 1, also the smaller the batch the less accurate the estimate of the gradient will be.
  history <- model %>% fit(
    partial_train_data, partial_train_targets,
    validation_data = list(val_data, val_targets),
    epochs = num_epochs, batch_size = 128, verbose = 1
  )
  acc_history <- history$metrics$val_categorical_accuracy
  all_acc_histories <- rbind(all_acc_histories, acc_history)
}

processing fold # 1 
Epoch 1/100

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979/979 [==============================] - 3s 3ms/step - loss: 1.3308 - categorical_accuracy: 0.4987

979/979 [==============================] - 4s 4ms/step - loss: 1.3308 - categorical_accuracy: 0.4987 - val_loss: 1.4802 - val_categorical_accuracy: 0.4906
Epoch 2/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.8868 - categorical_accuracy: 0.6612 - val_loss: 0.8707 - val_categorical_accuracy: 0.6634
Epoch 3/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.7423 - categorical_accuracy: 0.7166

979/979 [==============================] - 4s 4ms/step - loss: 0.7423 - categorical_accuracy: 0.7166 - val_loss: 0.9314 - val_categorical_accuracy: 0.6488
Epoch 4/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.6685 - categorical_accuracy: 0.7451

979/979 [==============================] - 4s 4ms/step - loss: 0.6685 - categorical_accuracy: 0.7451 - val_loss: 0.7828 - val_categorical_accuracy: 0.6995
Epoch 5/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.6156 - categorical_accuracy: 0.7668

979/979 [==============================] - 4s 4ms/step - loss: 0.6156 - categorical_accuracy: 0.7668 - val_loss: 0.5915 - val_categorical_accuracy: 0.7745
Epoch 6/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.5775 - categorical_accuracy: 0.7806 - val_loss: 0.5987 - val_categorical_accuracy: 0.7739
Epoch 7/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.5483 - categorical_accuracy: 0.7940 - val_loss: 0.7945 - val_categorical_accuracy: 0.7086
Epoch 8/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.5230 - categorical_accuracy: 0.8028

979/979 [==============================] - 4s 4ms/step - loss: 0.5230 - categorical_accuracy: 0.8028 - val_loss: 0.5592 - val_categorical_accuracy: 0.7938
Epoch 9/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.5015 - categorical_accuracy: 0.8116

979/979 [==============================] - 4s 4ms/step - loss: 0.5015 - categorical_accuracy: 0.8116 - val_loss: 0.6188 - val_categorical_accuracy: 0.7668
Epoch 10/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4849 - categorical_accuracy: 0.8174 - val_loss: 0.5202 - val_categorical_accuracy: 0.8014
Epoch 11/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4695 - categorical_accuracy: 0.8236 - val_loss: 0.6156 - val_categorical_accuracy: 0.7705
Epoch 12/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4563 - categorical_accuracy: 0.8294 - val_loss: 0.5278 - val_categorical_accuracy: 0.8093
Epoch 13/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.4454 - categorical_accuracy: 0.8328

979/979 [==============================] - 4s 4ms/step - loss: 0.4454 - categorical_accuracy: 0.8328 - val_loss: 0.5281 - val_categorical_accuracy: 0.8085
Epoch 14/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4339 - categorical_accuracy: 0.8388 - val_loss: 0.5452 - val_categorical_accuracy: 0.8014
Epoch 15/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.4241 - categorical_accuracy: 0.8421

979/979 [==============================] - 4s 4ms/step - loss: 0.4241 - categorical_accuracy: 0.8421 - val_loss: 0.4743 - val_categorical_accuracy: 0.8247
Epoch 16/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.4158 - categorical_accuracy: 0.8451

979/979 [==============================] - 4s 4ms/step - loss: 0.4158 - categorical_accuracy: 0.8451 - val_loss: 0.5431 - val_categorical_accuracy: 0.8052
Epoch 17/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4079 - categorical_accuracy: 0.8484 - val_loss: 0.4963 - val_categorical_accuracy: 0.8220
Epoch 18/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3983 - categorical_accuracy: 0.8515

979/979 [==============================] - 4s 4ms/step - loss: 0.3983 - categorical_accuracy: 0.8515 - val_loss: 0.4777 - val_categorical_accuracy: 0.8297
Epoch 19/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3938 - categorical_accuracy: 0.8539

979/979 [==============================] - 4s 4ms/step - loss: 0.3938 - categorical_accuracy: 0.8539 - val_loss: 0.4449 - val_categorical_accuracy: 0.8335
Epoch 20/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3893 - categorical_accuracy: 0.8562

979/979 [==============================] - 4s 4ms/step - loss: 0.3893 - categorical_accuracy: 0.8562 - val_loss: 0.4401 - val_categorical_accuracy: 0.8376
Epoch 21/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3802 - categorical_accuracy: 0.8590 - val_loss: 0.4564 - val_categorical_accuracy: 0.8323
Epoch 22/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3736 - categorical_accuracy: 0.8625 - val_loss: 0.4297 - val_categorical_accuracy: 0.8415
Epoch 23/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3682 - categorical_accuracy: 0.8638

979/979 [==============================] - 4s 4ms/step - loss: 0.3682 - categorical_accuracy: 0.8638 - val_loss: 0.4440 - val_categorical_accuracy: 0.8381
Epoch 24/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3642 - categorical_accuracy: 0.8659

979/979 [==============================] - 4s 4ms/step - loss: 0.3642 - categorical_accuracy: 0.8659 - val_loss: 0.5204 - val_categorical_accuracy: 0.8165
Epoch 25/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3607 - categorical_accuracy: 0.8675 - val_loss: 0.5058 - val_categorical_accuracy: 0.8238
Epoch 26/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.3569 - categorical_accuracy: 0.8692 - val_loss: 0.4159 - val_categorical_accuracy: 0.8523
Epoch 27/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3494 - categorical_accuracy: 0.8718

979/979 [==============================] - 4s 4ms/step - loss: 0.3494 - categorical_accuracy: 0.8718 - val_loss: 0.4263 - val_categorical_accuracy: 0.8472
Epoch 28/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.3487 - categorical_accuracy: 0.8724 - val_loss: 0.4093 - val_categorical_accuracy: 0.8554
Epoch 29/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3417 - categorical_accuracy: 0.8752 - val_loss: 0.4618 - val_categorical_accuracy: 0.8327
Epoch 30/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3412 - categorical_accuracy: 0.8754

979/979 [==============================] - 4s 5ms/step - loss: 0.3412 - categorical_accuracy: 0.8754 - val_loss: 0.3955 - val_categorical_accuracy: 0.8560
Epoch 31/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.3370 - categorical_accuracy: 0.8763 - val_loss: 0.4063 - val_categorical_accuracy: 0.8526
Epoch 32/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.3329 - categorical_accuracy: 0.8782 - val_loss: 0.4094 - val_categorical_accuracy: 0.8538
Epoch 33/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.3340 - categorical_accuracy: 0.8772 - val_loss: 0.3864 - val_categorical_accuracy: 0.8647
Epoch 34/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3282 - categorical_accuracy: 0.8802 - val_loss: 0.3954 - val_categorical_accuracy: 0.8592
Epoch 35/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.3264 - categorical_accuracy: 0.8810 - val_loss: 0.5086 - val_categorical_accuracy: 0.8181
Epoch 36/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3250 - categorical_accuracy: 0.8811 - val_loss: 0.4580 - val_categorical_accuracy: 0.8422
Epoch 37/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3195 - categorical_accuracy: 0.8824

979/979 [==============================] - 4s 5ms/step - loss: 0.3195 - categorical_accuracy: 0.8824 - val_loss: 0.3987 - val_categorical_accuracy: 0.8598
Epoch 38/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3196 - categorical_accuracy: 0.8842 - val_loss: 0.4358 - val_categorical_accuracy: 0.8453
Epoch 39/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3155 - categorical_accuracy: 0.8854 - val_loss: 0.3844 - val_categorical_accuracy: 0.8651
Epoch 40/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3137 - categorical_accuracy: 0.8855 - val_loss: 0.4041 - val_categorical_accuracy: 0.8563
Epoch 41/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3101 - categorical_accuracy: 0.8869 - val_loss: 0.4010 - val_categorical_accuracy: 0.8588
Epoch 42/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.3105 - categorical_accuracy: 0.8869 - val_loss: 0.4967 - val_categorical_accuracy: 0.8320
Epoch 43/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3110 - categorical_accuracy: 0.8859 - val_loss: 0.3968 - val_categorical_accuracy: 0.8560
Epoch 44/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.3043 - categorical_accuracy: 0.8898 - val_loss: 0.4398 - val_categorical_accuracy: 0.8505
Epoch 45/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.3031 - categorical_accuracy: 0.8887 - val_loss: 0.5031 - val_categorical_accuracy: 0.8313
Epoch 46/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.3015 - categorical_accuracy: 0.8902 - val_loss: 0.4148 - val_categorical_accuracy: 0.8572
Epoch 47/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3017 - categorical_accuracy: 0.8902

979/979 [==============================] - 4s 5ms/step - loss: 0.3017 - categorical_accuracy: 0.8902 - val_loss: 0.3867 - val_categorical_accuracy: 0.8671
Epoch 48/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2986 - categorical_accuracy: 0.8917

979/979 [==============================] - 4s 4ms/step - loss: 0.2986 - categorical_accuracy: 0.8917 - val_loss: 0.4056 - val_categorical_accuracy: 0.8573
Epoch 49/100

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979/979 [==============================] - 5s 5ms/step - loss: 0.2970 - categorical_accuracy: 0.8928 - val_loss: 0.3708 - val_categorical_accuracy: 0.8696
Epoch 50/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2978 - categorical_accuracy: 0.8925 - val_loss: 0.3921 - val_categorical_accuracy: 0.8603
Epoch 51/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2931 - categorical_accuracy: 0.8933

979/979 [==============================] - 4s 4ms/step - loss: 0.2931 - categorical_accuracy: 0.8933 - val_loss: 0.3764 - val_categorical_accuracy: 0.8707
Epoch 52/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2938 - categorical_accuracy: 0.8941

979/979 [==============================] - 4s 5ms/step - loss: 0.2938 - categorical_accuracy: 0.8941 - val_loss: 0.3710 - val_categorical_accuracy: 0.8717
Epoch 53/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2919 - categorical_accuracy: 0.8950 - val_loss: 0.4237 - val_categorical_accuracy: 0.8557
Epoch 54/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.2880 - categorical_accuracy: 0.8968 - val_loss: 0.4083 - val_categorical_accuracy: 0.8624
Epoch 55/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2889 - categorical_accuracy: 0.8954

979/979 [==============================] - 4s 4ms/step - loss: 0.2889 - categorical_accuracy: 0.8954 - val_loss: 0.4091 - val_categorical_accuracy: 0.8720
Epoch 56/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2882 - categorical_accuracy: 0.8968

979/979 [==============================] - 4s 4ms/step - loss: 0.2882 - categorical_accuracy: 0.8968 - val_loss: 0.4621 - val_categorical_accuracy: 0.8419
Epoch 57/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2868 - categorical_accuracy: 0.8967 - val_loss: 0.3860 - val_categorical_accuracy: 0.8680
Epoch 58/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2884 - categorical_accuracy: 0.8957 - val_loss: 0.4096 - val_categorical_accuracy: 0.8587
Epoch 59/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2864 - categorical_accuracy: 0.8976 - val_loss: 0.4123 - val_categorical_accuracy: 0.8575
Epoch 60/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2818 - categorical_accuracy: 0.8982 - val_loss: 0.3888 - val_categorical_accuracy: 0.8703
Epoch 61/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2812 - categorical_accuracy: 0.8989

979/979 [==============================] - 5s 5ms/step - loss: 0.2812 - categorical_accuracy: 0.8989 - val_loss: 0.3698 - val_categorical_accuracy: 0.8748
Epoch 62/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2798 - categorical_accuracy: 0.8990

979/979 [==============================] - 4s 4ms/step - loss: 0.2798 - categorical_accuracy: 0.8990 - val_loss: 0.3937 - val_categorical_accuracy: 0.8678
Epoch 63/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2809 - categorical_accuracy: 0.8982

979/979 [==============================] - 4s 4ms/step - loss: 0.2809 - categorical_accuracy: 0.8982 - val_loss: 0.4613 - val_categorical_accuracy: 0.8451
Epoch 64/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2800 - categorical_accuracy: 0.8982 - val_loss: 0.3809 - val_categorical_accuracy: 0.8691
Epoch 65/100

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979/979 [==============================] - 5s 5ms/step - loss: 0.2783 - categorical_accuracy: 0.8998 - val_loss: 0.4081 - val_categorical_accuracy: 0.8663
Epoch 66/100

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979/979 [==============================] - 3s 4ms/step - loss: 0.2758 - categorical_accuracy: 0.9012

979/979 [==============================] - 5s 5ms/step - loss: 0.2758 - categorical_accuracy: 0.9012 - val_loss: 0.3609 - val_categorical_accuracy: 0.8746
Epoch 67/100

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979/979 [==============================] - 5s 5ms/step - loss: 0.2752 - categorical_accuracy: 0.9010 - val_loss: 0.4651 - val_categorical_accuracy: 0.8497
Epoch 68/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2762 - categorical_accuracy: 0.9013

979/979 [==============================] - 5s 5ms/step - loss: 0.2762 - categorical_accuracy: 0.9013 - val_loss: 0.3760 - val_categorical_accuracy: 0.8711
Epoch 69/100

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979/979 [==============================] - 3s 4ms/step - loss: 0.2742 - categorical_accuracy: 0.9016

979/979 [==============================] - 5s 5ms/step - loss: 0.2742 - categorical_accuracy: 0.9016 - val_loss: 0.3617 - val_categorical_accuracy: 0.8778
Epoch 70/100

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979/979 [==============================] - 3s 4ms/step - loss: 0.2711 - categorical_accuracy: 0.9031

979/979 [==============================] - 5s 5ms/step - loss: 0.2711 - categorical_accuracy: 0.9031 - val_loss: 0.3808 - val_categorical_accuracy: 0.8733
Epoch 71/100

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979/979 [==============================] - 5s 5ms/step - loss: 0.2711 - categorical_accuracy: 0.9029 - val_loss: 0.3836 - val_categorical_accuracy: 0.8720
Epoch 72/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2706 - categorical_accuracy: 0.9041

979/979 [==============================] - 5s 5ms/step - loss: 0.2706 - categorical_accuracy: 0.9041 - val_loss: 0.3747 - val_categorical_accuracy: 0.8749
Epoch 73/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2710 - categorical_accuracy: 0.9026

979/979 [==============================] - 5s 5ms/step - loss: 0.2710 - categorical_accuracy: 0.9026 - val_loss: 0.3631 - val_categorical_accuracy: 0.8769
Epoch 74/100

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979/979 [==============================] - 5s 5ms/step - loss: 0.2692 - categorical_accuracy: 0.9034 - val_loss: 0.3780 - val_categorical_accuracy: 0.8710
Epoch 75/100

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979/979 [==============================] - 5s 5ms/step - loss: 0.2683 - categorical_accuracy: 0.9046 - val_loss: 0.4474 - val_categorical_accuracy: 0.8514
Epoch 76/100

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979/979 [==============================] - 5s 5ms/step - loss: 0.2678 - categorical_accuracy: 0.9042 - val_loss: 0.4367 - val_categorical_accuracy: 0.8588
Epoch 77/100

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979/979 [==============================] - 5s 5ms/step - loss: 0.2694 - categorical_accuracy: 0.9039 - val_loss: 0.4107 - val_categorical_accuracy: 0.8644
Epoch 78/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2655 - categorical_accuracy: 0.9052

979/979 [==============================] - 5s 5ms/step - loss: 0.2655 - categorical_accuracy: 0.9052 - val_loss: 0.3629 - val_categorical_accuracy: 0.8769
Epoch 79/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2680 - categorical_accuracy: 0.9032

979/979 [==============================] - 5s 5ms/step - loss: 0.2680 - categorical_accuracy: 0.9032 - val_loss: 0.4173 - val_categorical_accuracy: 0.8595
Epoch 80/100

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979/979 [==============================] - 3s 4ms/step - loss: 0.2671 - categorical_accuracy: 0.9046

979/979 [==============================] - 5s 5ms/step - loss: 0.2671 - categorical_accuracy: 0.9046 - val_loss: 0.3596 - val_categorical_accuracy: 0.8799
Epoch 81/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2661 - categorical_accuracy: 0.9051

979/979 [==============================] - 5s 5ms/step - loss: 0.2661 - categorical_accuracy: 0.9051 - val_loss: 0.3846 - val_categorical_accuracy: 0.8730
Epoch 82/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2635 - categorical_accuracy: 0.9059

979/979 [==============================] - 4s 5ms/step - loss: 0.2635 - categorical_accuracy: 0.9059 - val_loss: 0.3810 - val_categorical_accuracy: 0.8756
Epoch 83/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2640 - categorical_accuracy: 0.9051

979/979 [==============================] - 5s 5ms/step - loss: 0.2640 - categorical_accuracy: 0.9051 - val_loss: 0.3828 - val_categorical_accuracy: 0.8742
Epoch 84/100

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979/979 [==============================] - 5s 5ms/step - loss: 0.2633 - categorical_accuracy: 0.9067 - val_loss: 0.3660 - val_categorical_accuracy: 0.8791
Epoch 85/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2611 - categorical_accuracy: 0.9066

979/979 [==============================] - 4s 4ms/step - loss: 0.2611 - categorical_accuracy: 0.9066 - val_loss: 0.4073 - val_categorical_accuracy: 0.8706
Epoch 86/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2651 - categorical_accuracy: 0.9058

979/979 [==============================] - 4s 4ms/step - loss: 0.2651 - categorical_accuracy: 0.9058 - val_loss: 0.3835 - val_categorical_accuracy: 0.8696
Epoch 87/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2607 - categorical_accuracy: 0.9075 - val_loss: 0.3754 - val_categorical_accuracy: 0.8766
Epoch 88/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2613 - categorical_accuracy: 0.9069

979/979 [==============================] - 4s 4ms/step - loss: 0.2613 - categorical_accuracy: 0.9069 - val_loss: 0.3784 - val_categorical_accuracy: 0.8745
Epoch 89/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2589 - categorical_accuracy: 0.9078

979/979 [==============================] - 4s 4ms/step - loss: 0.2589 - categorical_accuracy: 0.9078 - val_loss: 0.3956 - val_categorical_accuracy: 0.8710
Epoch 90/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2582 - categorical_accuracy: 0.9081

979/979 [==============================] - 4s 4ms/step - loss: 0.2582 - categorical_accuracy: 0.9081 - val_loss: 0.3710 - val_categorical_accuracy: 0.8766
Epoch 91/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2588 - categorical_accuracy: 0.9071

979/979 [==============================] - 4s 4ms/step - loss: 0.2588 - categorical_accuracy: 0.9071 - val_loss: 0.4266 - val_categorical_accuracy: 0.8610
Epoch 92/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2572 - categorical_accuracy: 0.9082 - val_loss: 0.4304 - val_categorical_accuracy: 0.8665
Epoch 93/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2612 - categorical_accuracy: 0.9070

979/979 [==============================] - 4s 4ms/step - loss: 0.2612 - categorical_accuracy: 0.9070 - val_loss: 0.3740 - val_categorical_accuracy: 0.8754
Epoch 94/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2549 - categorical_accuracy: 0.9089

979/979 [==============================] - 4s 4ms/step - loss: 0.2549 - categorical_accuracy: 0.9089 - val_loss: 0.3655 - val_categorical_accuracy: 0.8818
Epoch 95/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2565 - categorical_accuracy: 0.9094 - val_loss: 0.3930 - val_categorical_accuracy: 0.8728
Epoch 96/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2558 - categorical_accuracy: 0.9095 - val_loss: 0.3747 - val_categorical_accuracy: 0.8783
Epoch 97/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2545 - categorical_accuracy: 0.9094

979/979 [==============================] - 4s 4ms/step - loss: 0.2545 - categorical_accuracy: 0.9094 - val_loss: 0.4013 - val_categorical_accuracy: 0.8649
Epoch 98/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2568 - categorical_accuracy: 0.9078

979/979 [==============================] - 4s 4ms/step - loss: 0.2568 - categorical_accuracy: 0.9078 - val_loss: 0.3722 - val_categorical_accuracy: 0.8790
Epoch 99/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2560 - categorical_accuracy: 0.9089 - val_loss: 0.3890 - val_categorical_accuracy: 0.8724
Epoch 100/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2570 - categorical_accuracy: 0.9083

979/979 [==============================] - 4s 4ms/step - loss: 0.2570 - categorical_accuracy: 0.9083 - val_loss: 0.3778 - val_categorical_accuracy: 0.8770
processing fold # 2 
Epoch 1/100

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979/979 [==============================] - 3s 3ms/step - loss: 1.3461 - categorical_accuracy: 0.4907

979/979 [==============================] - 5s 5ms/step - loss: 1.3461 - categorical_accuracy: 0.4907 - val_loss: 1.0576 - val_categorical_accuracy: 0.5875
Epoch 2/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.8975 - categorical_accuracy: 0.6566 - val_loss: 0.8061 - val_categorical_accuracy: 0.6914
Epoch 3/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.7526 - categorical_accuracy: 0.7096 - val_loss: 0.7482 - val_categorical_accuracy: 0.7167
Epoch 4/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.6712 - categorical_accuracy: 0.7423

979/979 [==============================] - 4s 4ms/step - loss: 0.6712 - categorical_accuracy: 0.7423 - val_loss: 0.6369 - val_categorical_accuracy: 0.7558
Epoch 5/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.6169 - categorical_accuracy: 0.7635

979/979 [==============================] - 5s 5ms/step - loss: 0.6169 - categorical_accuracy: 0.7635 - val_loss: 0.8520 - val_categorical_accuracy: 0.6764
Epoch 6/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.5738 - categorical_accuracy: 0.7802 - val_loss: 0.6195 - val_categorical_accuracy: 0.7654
Epoch 7/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.5433 - categorical_accuracy: 0.7926 - val_loss: 0.6173 - val_categorical_accuracy: 0.7681
Epoch 8/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.5169 - categorical_accuracy: 0.8031

979/979 [==============================] - 4s 4ms/step - loss: 0.5169 - categorical_accuracy: 0.8031 - val_loss: 0.5529 - val_categorical_accuracy: 0.7937
Epoch 9/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4988 - categorical_accuracy: 0.8113 - val_loss: 0.5718 - val_categorical_accuracy: 0.7887
Epoch 10/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4792 - categorical_accuracy: 0.8196 - val_loss: 0.5915 - val_categorical_accuracy: 0.7783
Epoch 11/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4635 - categorical_accuracy: 0.8246 - val_loss: 0.5762 - val_categorical_accuracy: 0.7874
Epoch 12/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4505 - categorical_accuracy: 0.8305 - val_loss: 0.4919 - val_categorical_accuracy: 0.8162
Epoch 13/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.4378 - categorical_accuracy: 0.8357 - val_loss: 0.5057 - val_categorical_accuracy: 0.8139
Epoch 14/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4283 - categorical_accuracy: 0.8396 - val_loss: 0.4947 - val_categorical_accuracy: 0.8188
Epoch 15/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.4180 - categorical_accuracy: 0.8441

979/979 [==============================] - 4s 4ms/step - loss: 0.4180 - categorical_accuracy: 0.8441 - val_loss: 0.4705 - val_categorical_accuracy: 0.8222
Epoch 16/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4081 - categorical_accuracy: 0.8480 - val_loss: 0.5160 - val_categorical_accuracy: 0.8099
Epoch 17/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4017 - categorical_accuracy: 0.8494 - val_loss: 0.4271 - val_categorical_accuracy: 0.8454
Epoch 18/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3928 - categorical_accuracy: 0.8535

979/979 [==============================] - 4s 4ms/step - loss: 0.3928 - categorical_accuracy: 0.8535 - val_loss: 0.4684 - val_categorical_accuracy: 0.8261
Epoch 19/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3864 - categorical_accuracy: 0.8559 - val_loss: 0.4442 - val_categorical_accuracy: 0.8375
Epoch 20/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3792 - categorical_accuracy: 0.8594 - val_loss: 0.4434 - val_categorical_accuracy: 0.8425
Epoch 21/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.3712 - categorical_accuracy: 0.8620 - val_loss: 0.4508 - val_categorical_accuracy: 0.8332
Epoch 22/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3677 - categorical_accuracy: 0.8630 - val_loss: 0.4392 - val_categorical_accuracy: 0.8419
Epoch 23/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3636 - categorical_accuracy: 0.8647 - val_loss: 0.4125 - val_categorical_accuracy: 0.8500
Epoch 24/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3589 - categorical_accuracy: 0.8662 - val_loss: 0.4213 - val_categorical_accuracy: 0.8459
Epoch 25/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3540 - categorical_accuracy: 0.8691 - val_loss: 0.4079 - val_categorical_accuracy: 0.8539
Epoch 26/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3492 - categorical_accuracy: 0.8704

979/979 [==============================] - 4s 4ms/step - loss: 0.3492 - categorical_accuracy: 0.8704 - val_loss: 0.4280 - val_categorical_accuracy: 0.8461
Epoch 27/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3431 - categorical_accuracy: 0.8737 - val_loss: 0.4324 - val_categorical_accuracy: 0.8475
Epoch 28/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3404 - categorical_accuracy: 0.8747 - val_loss: 0.4512 - val_categorical_accuracy: 0.8394
Epoch 29/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3380 - categorical_accuracy: 0.8757

979/979 [==============================] - 4s 4ms/step - loss: 0.3380 - categorical_accuracy: 0.8757 - val_loss: 0.3953 - val_categorical_accuracy: 0.8582
Epoch 30/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3328 - categorical_accuracy: 0.8765 - val_loss: 0.4552 - val_categorical_accuracy: 0.8444
Epoch 31/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3295 - categorical_accuracy: 0.8782 - val_loss: 0.4285 - val_categorical_accuracy: 0.8459
Epoch 32/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3279 - categorical_accuracy: 0.8796 - val_loss: 0.4088 - val_categorical_accuracy: 0.8512
Epoch 33/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3245 - categorical_accuracy: 0.8799

979/979 [==============================] - 4s 4ms/step - loss: 0.3245 - categorical_accuracy: 0.8799 - val_loss: 0.7025 - val_categorical_accuracy: 0.7611
Epoch 34/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.3211 - categorical_accuracy: 0.8823 - val_loss: 0.4284 - val_categorical_accuracy: 0.8535
Epoch 35/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3173 - categorical_accuracy: 0.8831 - val_loss: 0.4495 - val_categorical_accuracy: 0.8421
Epoch 36/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3171 - categorical_accuracy: 0.8834

979/979 [==============================] - 4s 4ms/step - loss: 0.3171 - categorical_accuracy: 0.8834 - val_loss: 0.4609 - val_categorical_accuracy: 0.8402
Epoch 37/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3131 - categorical_accuracy: 0.8848 - val_loss: 0.3836 - val_categorical_accuracy: 0.8657
Epoch 38/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3101 - categorical_accuracy: 0.8864 - val_loss: 0.3790 - val_categorical_accuracy: 0.8664
Epoch 39/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.3085 - categorical_accuracy: 0.8865 - val_loss: 0.4280 - val_categorical_accuracy: 0.8477
Epoch 40/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3067 - categorical_accuracy: 0.8874

979/979 [==============================] - 4s 4ms/step - loss: 0.3067 - categorical_accuracy: 0.8874 - val_loss: 0.3700 - val_categorical_accuracy: 0.8703
Epoch 41/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3044 - categorical_accuracy: 0.8882 - val_loss: 0.4024 - val_categorical_accuracy: 0.8588
Epoch 42/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3030 - categorical_accuracy: 0.8894 - val_loss: 0.3867 - val_categorical_accuracy: 0.8642
Epoch 43/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2975 - categorical_accuracy: 0.8911

979/979 [==============================] - 4s 4ms/step - loss: 0.2975 - categorical_accuracy: 0.8911 - val_loss: 0.3769 - val_categorical_accuracy: 0.8690
Epoch 44/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2978 - categorical_accuracy: 0.8918 - val_loss: 0.3943 - val_categorical_accuracy: 0.8667
Epoch 45/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2959 - categorical_accuracy: 0.8924 - val_loss: 0.3861 - val_categorical_accuracy: 0.8683
Epoch 46/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2956 - categorical_accuracy: 0.8926 - val_loss: 0.4108 - val_categorical_accuracy: 0.8556
Epoch 47/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2919 - categorical_accuracy: 0.8932

979/979 [==============================] - 4s 4ms/step - loss: 0.2919 - categorical_accuracy: 0.8932 - val_loss: 0.3924 - val_categorical_accuracy: 0.8672
Epoch 48/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2929 - categorical_accuracy: 0.8940 - val_loss: 0.4134 - val_categorical_accuracy: 0.8557
Epoch 49/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2882 - categorical_accuracy: 0.8950 - val_loss: 0.3793 - val_categorical_accuracy: 0.8704
Epoch 50/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2894 - categorical_accuracy: 0.8945

979/979 [==============================] - 4s 4ms/step - loss: 0.2894 - categorical_accuracy: 0.8945 - val_loss: 0.4037 - val_categorical_accuracy: 0.8651
Epoch 51/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2857 - categorical_accuracy: 0.8960 - val_loss: 0.3811 - val_categorical_accuracy: 0.8700
Epoch 52/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2867 - categorical_accuracy: 0.8954 - val_loss: 0.3866 - val_categorical_accuracy: 0.8692
Epoch 53/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.2839 - categorical_accuracy: 0.8973 - val_loss: 0.4096 - val_categorical_accuracy: 0.8595
Epoch 54/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2804 - categorical_accuracy: 0.8977

979/979 [==============================] - 4s 4ms/step - loss: 0.2804 - categorical_accuracy: 0.8977 - val_loss: 0.3837 - val_categorical_accuracy: 0.8691
Epoch 55/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2828 - categorical_accuracy: 0.8984 - val_loss: 0.3931 - val_categorical_accuracy: 0.8663
Epoch 56/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2821 - categorical_accuracy: 0.8976 - val_loss: 0.3903 - val_categorical_accuracy: 0.8669
Epoch 57/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2797 - categorical_accuracy: 0.8982 - val_loss: 0.3608 - val_categorical_accuracy: 0.8759
Epoch 58/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2752 - categorical_accuracy: 0.9005

979/979 [==============================] - 4s 4ms/step - loss: 0.2752 - categorical_accuracy: 0.9005 - val_loss: 0.4464 - val_categorical_accuracy: 0.8494
Epoch 59/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2773 - categorical_accuracy: 0.8996 - val_loss: 0.3847 - val_categorical_accuracy: 0.8702
Epoch 60/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2756 - categorical_accuracy: 0.8993 - val_loss: 0.3977 - val_categorical_accuracy: 0.8616
Epoch 61/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2759 - categorical_accuracy: 0.8996

979/979 [==============================] - 4s 4ms/step - loss: 0.2759 - categorical_accuracy: 0.8996 - val_loss: 0.3637 - val_categorical_accuracy: 0.8751
Epoch 62/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2704 - categorical_accuracy: 0.9016

979/979 [==============================] - 4s 5ms/step - loss: 0.2704 - categorical_accuracy: 0.9016 - val_loss: 0.3740 - val_categorical_accuracy: 0.8791
Epoch 63/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2732 - categorical_accuracy: 0.9012 - val_loss: 0.4029 - val_categorical_accuracy: 0.8646
Epoch 64/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2719 - categorical_accuracy: 0.9013

979/979 [==============================] - 4s 4ms/step - loss: 0.2719 - categorical_accuracy: 0.9013 - val_loss: 0.3765 - val_categorical_accuracy: 0.8703
Epoch 65/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2707 - categorical_accuracy: 0.9018

979/979 [==============================] - 4s 4ms/step - loss: 0.2707 - categorical_accuracy: 0.9018 - val_loss: 0.4136 - val_categorical_accuracy: 0.8645
Epoch 66/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2711 - categorical_accuracy: 0.9020

979/979 [==============================] - 4s 4ms/step - loss: 0.2711 - categorical_accuracy: 0.9020 - val_loss: 0.3673 - val_categorical_accuracy: 0.8775
Epoch 67/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2662 - categorical_accuracy: 0.9039

979/979 [==============================] - 4s 4ms/step - loss: 0.2662 - categorical_accuracy: 0.9039 - val_loss: 0.3745 - val_categorical_accuracy: 0.8746
Epoch 68/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2695 - categorical_accuracy: 0.9027

979/979 [==============================] - 4s 4ms/step - loss: 0.2695 - categorical_accuracy: 0.9027 - val_loss: 0.3568 - val_categorical_accuracy: 0.8786
Epoch 69/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2644 - categorical_accuracy: 0.9046

979/979 [==============================] - 4s 4ms/step - loss: 0.2644 - categorical_accuracy: 0.9046 - val_loss: 0.3769 - val_categorical_accuracy: 0.8726
Epoch 70/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2642 - categorical_accuracy: 0.9047 - val_loss: 0.3778 - val_categorical_accuracy: 0.8747
Epoch 71/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2672 - categorical_accuracy: 0.9037 - val_loss: 0.3634 - val_categorical_accuracy: 0.8790
Epoch 72/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2640 - categorical_accuracy: 0.9048

979/979 [==============================] - 4s 4ms/step - loss: 0.2640 - categorical_accuracy: 0.9048 - val_loss: 0.3651 - val_categorical_accuracy: 0.8808
Epoch 73/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2621 - categorical_accuracy: 0.9053

979/979 [==============================] - 4s 4ms/step - loss: 0.2621 - categorical_accuracy: 0.9053 - val_loss: 0.3591 - val_categorical_accuracy: 0.8815
Epoch 74/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2642 - categorical_accuracy: 0.9051 - val_loss: 0.3565 - val_categorical_accuracy: 0.8813
Epoch 75/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2634 - categorical_accuracy: 0.9047

979/979 [==============================] - 4s 4ms/step - loss: 0.2634 - categorical_accuracy: 0.9047 - val_loss: 0.3972 - val_categorical_accuracy: 0.8646
Epoch 76/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2636 - categorical_accuracy: 0.9059

979/979 [==============================] - 4s 4ms/step - loss: 0.2636 - categorical_accuracy: 0.9059 - val_loss: 0.3540 - val_categorical_accuracy: 0.8800
Epoch 77/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2593 - categorical_accuracy: 0.9064

979/979 [==============================] - 4s 4ms/step - loss: 0.2593 - categorical_accuracy: 0.9064 - val_loss: 0.4063 - val_categorical_accuracy: 0.8648
Epoch 78/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2607 - categorical_accuracy: 0.9064 - val_loss: 0.4177 - val_categorical_accuracy: 0.8562
Epoch 79/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2591 - categorical_accuracy: 0.9069 - val_loss: 0.3613 - val_categorical_accuracy: 0.8781
Epoch 80/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2569 - categorical_accuracy: 0.9077

979/979 [==============================] - 4s 4ms/step - loss: 0.2569 - categorical_accuracy: 0.9077 - val_loss: 0.4279 - val_categorical_accuracy: 0.8570
Epoch 81/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2578 - categorical_accuracy: 0.9072

979/979 [==============================] - 4s 4ms/step - loss: 0.2578 - categorical_accuracy: 0.9072 - val_loss: 0.3862 - val_categorical_accuracy: 0.8672
Epoch 82/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2553 - categorical_accuracy: 0.9084

979/979 [==============================] - 4s 4ms/step - loss: 0.2553 - categorical_accuracy: 0.9084 - val_loss: 0.4174 - val_categorical_accuracy: 0.8640
Epoch 83/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2582 - categorical_accuracy: 0.9071

979/979 [==============================] - 4s 4ms/step - loss: 0.2582 - categorical_accuracy: 0.9071 - val_loss: 0.3625 - val_categorical_accuracy: 0.8749
Epoch 84/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2574 - categorical_accuracy: 0.9077

979/979 [==============================] - 4s 4ms/step - loss: 0.2574 - categorical_accuracy: 0.9077 - val_loss: 0.3913 - val_categorical_accuracy: 0.8722
Epoch 85/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2588 - categorical_accuracy: 0.9070

979/979 [==============================] - 4s 4ms/step - loss: 0.2588 - categorical_accuracy: 0.9070 - val_loss: 0.3757 - val_categorical_accuracy: 0.8758
Epoch 86/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2559 - categorical_accuracy: 0.9077

979/979 [==============================] - 4s 4ms/step - loss: 0.2559 - categorical_accuracy: 0.9077 - val_loss: 0.3851 - val_categorical_accuracy: 0.8720
Epoch 87/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2536 - categorical_accuracy: 0.9092

979/979 [==============================] - 4s 4ms/step - loss: 0.2536 - categorical_accuracy: 0.9092 - val_loss: 0.4026 - val_categorical_accuracy: 0.8732
Epoch 88/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2544 - categorical_accuracy: 0.9091

979/979 [==============================] - 4s 4ms/step - loss: 0.2544 - categorical_accuracy: 0.9091 - val_loss: 0.3820 - val_categorical_accuracy: 0.8752
Epoch 89/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2516 - categorical_accuracy: 0.9091 - val_loss: 0.3564 - val_categorical_accuracy: 0.8840
Epoch 90/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2554 - categorical_accuracy: 0.9081 - val_loss: 0.3743 - val_categorical_accuracy: 0.8773
Epoch 91/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2510 - categorical_accuracy: 0.9094

979/979 [==============================] - 4s 4ms/step - loss: 0.2510 - categorical_accuracy: 0.9094 - val_loss: 0.3654 - val_categorical_accuracy: 0.8805
Epoch 92/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2517 - categorical_accuracy: 0.9096 - val_loss: 0.3858 - val_categorical_accuracy: 0.8716
Epoch 93/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2536 - categorical_accuracy: 0.9093

979/979 [==============================] - 4s 4ms/step - loss: 0.2536 - categorical_accuracy: 0.9093 - val_loss: 0.3829 - val_categorical_accuracy: 0.8758
Epoch 94/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2520 - categorical_accuracy: 0.9091

979/979 [==============================] - 4s 4ms/step - loss: 0.2520 - categorical_accuracy: 0.9091 - val_loss: 0.3776 - val_categorical_accuracy: 0.8763
Epoch 95/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2515 - categorical_accuracy: 0.9091

979/979 [==============================] - 4s 4ms/step - loss: 0.2515 - categorical_accuracy: 0.9091 - val_loss: 0.4790 - val_categorical_accuracy: 0.8416
Epoch 96/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2503 - categorical_accuracy: 0.9105

979/979 [==============================] - 4s 4ms/step - loss: 0.2503 - categorical_accuracy: 0.9105 - val_loss: 0.4283 - val_categorical_accuracy: 0.8578
Epoch 97/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2501 - categorical_accuracy: 0.9107

979/979 [==============================] - 4s 4ms/step - loss: 0.2501 - categorical_accuracy: 0.9107 - val_loss: 0.3576 - val_categorical_accuracy: 0.8838
Epoch 98/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2481 - categorical_accuracy: 0.9103

979/979 [==============================] - 4s 4ms/step - loss: 0.2481 - categorical_accuracy: 0.9103 - val_loss: 0.3576 - val_categorical_accuracy: 0.8835
Epoch 99/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2474 - categorical_accuracy: 0.9114

979/979 [==============================] - 4s 4ms/step - loss: 0.2474 - categorical_accuracy: 0.9114 - val_loss: 0.3941 - val_categorical_accuracy: 0.8754
Epoch 100/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2484 - categorical_accuracy: 0.9110

979/979 [==============================] - 4s 4ms/step - loss: 0.2484 - categorical_accuracy: 0.9110 - val_loss: 0.3720 - val_categorical_accuracy: 0.8772
processing fold # 3 
Epoch 1/100

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979/979 [==============================] - 5s 5ms/step - loss: 1.3490 - categorical_accuracy: 0.4929 - val_loss: 1.0498 - val_categorical_accuracy: 0.5985
Epoch 2/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.8922 - categorical_accuracy: 0.6614 - val_loss: 0.8844 - val_categorical_accuracy: 0.6563
Epoch 3/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.7516 - categorical_accuracy: 0.7117

979/979 [==============================] - 4s 4ms/step - loss: 0.7516 - categorical_accuracy: 0.7117 - val_loss: 0.8678 - val_categorical_accuracy: 0.6679
Epoch 4/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.6810 - categorical_accuracy: 0.7400

979/979 [==============================] - 4s 5ms/step - loss: 0.6810 - categorical_accuracy: 0.7400 - val_loss: 0.6859 - val_categorical_accuracy: 0.7351
Epoch 5/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.6261 - categorical_accuracy: 0.7629

979/979 [==============================] - 4s 4ms/step - loss: 0.6261 - categorical_accuracy: 0.7629 - val_loss: 0.7075 - val_categorical_accuracy: 0.7262
Epoch 6/100

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979/979 [==============================] - 3s 4ms/step - loss: 0.5862 - categorical_accuracy: 0.7754

979/979 [==============================] - 5s 5ms/step - loss: 0.5862 - categorical_accuracy: 0.7754 - val_loss: 0.5971 - val_categorical_accuracy: 0.7700
Epoch 7/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.5572 - categorical_accuracy: 0.7881

979/979 [==============================] - 4s 5ms/step - loss: 0.5572 - categorical_accuracy: 0.7881 - val_loss: 0.5372 - val_categorical_accuracy: 0.7969
Epoch 8/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.5249 - categorical_accuracy: 0.8002 - val_loss: 0.6142 - val_categorical_accuracy: 0.7666
Epoch 9/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.5092 - categorical_accuracy: 0.8072 - val_loss: 0.5953 - val_categorical_accuracy: 0.7809
Epoch 10/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.4871 - categorical_accuracy: 0.8176

979/979 [==============================] - 4s 5ms/step - loss: 0.4871 - categorical_accuracy: 0.8176 - val_loss: 0.5216 - val_categorical_accuracy: 0.7978
Epoch 11/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.4727 - categorical_accuracy: 0.8217 - val_loss: 0.5071 - val_categorical_accuracy: 0.8134
Epoch 12/100

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979/979 [==============================] - 4s 5ms/step - loss: 0.4619 - categorical_accuracy: 0.8261 - val_loss: 0.4988 - val_categorical_accuracy: 0.8163
Epoch 13/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.4460 - categorical_accuracy: 0.8322

979/979 [==============================] - 5s 5ms/step - loss: 0.4460 - categorical_accuracy: 0.8322 - val_loss: 0.5042 - val_categorical_accuracy: 0.8132
Epoch 14/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4382 - categorical_accuracy: 0.8353 - val_loss: 0.5112 - val_categorical_accuracy: 0.8142
Epoch 15/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4289 - categorical_accuracy: 0.8388 - val_loss: 0.4895 - val_categorical_accuracy: 0.8198
Epoch 16/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.4175 - categorical_accuracy: 0.8436 - val_loss: 0.4538 - val_categorical_accuracy: 0.8327
Epoch 17/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.4078 - categorical_accuracy: 0.8485

979/979 [==============================] - 4s 4ms/step - loss: 0.4078 - categorical_accuracy: 0.8485 - val_loss: 0.4625 - val_categorical_accuracy: 0.8285
Epoch 18/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.4034 - categorical_accuracy: 0.8502

979/979 [==============================] - 4s 4ms/step - loss: 0.4034 - categorical_accuracy: 0.8502 - val_loss: 0.5552 - val_categorical_accuracy: 0.7953
Epoch 19/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3948 - categorical_accuracy: 0.8533 - val_loss: 0.4412 - val_categorical_accuracy: 0.8385
Epoch 20/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3861 - categorical_accuracy: 0.8567 - val_loss: 0.4368 - val_categorical_accuracy: 0.8391
Epoch 21/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3847 - categorical_accuracy: 0.8579

979/979 [==============================] - 4s 4ms/step - loss: 0.3847 - categorical_accuracy: 0.8579 - val_loss: 0.5144 - val_categorical_accuracy: 0.8150
Epoch 22/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3763 - categorical_accuracy: 0.8603

979/979 [==============================] - 4s 4ms/step - loss: 0.3763 - categorical_accuracy: 0.8603 - val_loss: 0.5608 - val_categorical_accuracy: 0.8027
Epoch 23/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3714 - categorical_accuracy: 0.8619 - val_loss: 0.5234 - val_categorical_accuracy: 0.8157
Epoch 24/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3653 - categorical_accuracy: 0.8650 - val_loss: 0.4452 - val_categorical_accuracy: 0.8426
Epoch 25/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3594 - categorical_accuracy: 0.8673

979/979 [==============================] - 4s 4ms/step - loss: 0.3594 - categorical_accuracy: 0.8673 - val_loss: 0.4272 - val_categorical_accuracy: 0.8398
Epoch 26/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3526 - categorical_accuracy: 0.8699

979/979 [==============================] - 4s 4ms/step - loss: 0.3526 - categorical_accuracy: 0.8699 - val_loss: 0.4087 - val_categorical_accuracy: 0.8521
Epoch 27/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3502 - categorical_accuracy: 0.8721

979/979 [==============================] - 4s 4ms/step - loss: 0.3502 - categorical_accuracy: 0.8721 - val_loss: 0.4789 - val_categorical_accuracy: 0.8281
Epoch 28/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3496 - categorical_accuracy: 0.8710

979/979 [==============================] - 4s 4ms/step - loss: 0.3496 - categorical_accuracy: 0.8710 - val_loss: 0.4824 - val_categorical_accuracy: 0.8265
Epoch 29/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3476 - categorical_accuracy: 0.8724

979/979 [==============================] - 4s 4ms/step - loss: 0.3476 - categorical_accuracy: 0.8724 - val_loss: 0.3949 - val_categorical_accuracy: 0.8572
Epoch 30/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3394 - categorical_accuracy: 0.8752 - val_loss: 0.4060 - val_categorical_accuracy: 0.8555
Epoch 31/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3363 - categorical_accuracy: 0.8769 - val_loss: 0.4018 - val_categorical_accuracy: 0.8575
Epoch 32/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3330 - categorical_accuracy: 0.8784

979/979 [==============================] - 4s 4ms/step - loss: 0.3330 - categorical_accuracy: 0.8784 - val_loss: 0.4244 - val_categorical_accuracy: 0.8465
Epoch 33/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3285 - categorical_accuracy: 0.8790

979/979 [==============================] - 4s 4ms/step - loss: 0.3285 - categorical_accuracy: 0.8790 - val_loss: 0.4446 - val_categorical_accuracy: 0.8375
Epoch 34/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3267 - categorical_accuracy: 0.8811 - val_loss: 0.4292 - val_categorical_accuracy: 0.8479
Epoch 35/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3223 - categorical_accuracy: 0.8822 - val_loss: 0.4009 - val_categorical_accuracy: 0.8602
Epoch 36/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3214 - categorical_accuracy: 0.8823

979/979 [==============================] - 4s 4ms/step - loss: 0.3214 - categorical_accuracy: 0.8823 - val_loss: 0.3968 - val_categorical_accuracy: 0.8627
Epoch 37/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3215 - categorical_accuracy: 0.8823

979/979 [==============================] - 4s 4ms/step - loss: 0.3215 - categorical_accuracy: 0.8823 - val_loss: 0.3663 - val_categorical_accuracy: 0.8701
Epoch 38/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3174 - categorical_accuracy: 0.8842 - val_loss: 0.3989 - val_categorical_accuracy: 0.8593
Epoch 39/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3142 - categorical_accuracy: 0.8852 - val_loss: 0.3963 - val_categorical_accuracy: 0.8580
Epoch 40/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3122 - categorical_accuracy: 0.8857

979/979 [==============================] - 4s 4ms/step - loss: 0.3122 - categorical_accuracy: 0.8857 - val_loss: 0.3887 - val_categorical_accuracy: 0.8638
Epoch 41/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3082 - categorical_accuracy: 0.8882

979/979 [==============================] - 4s 4ms/step - loss: 0.3082 - categorical_accuracy: 0.8882 - val_loss: 0.4296 - val_categorical_accuracy: 0.8513
Epoch 42/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3070 - categorical_accuracy: 0.8892 - val_loss: 0.4093 - val_categorical_accuracy: 0.8544
Epoch 43/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.3057 - categorical_accuracy: 0.8884 - val_loss: 0.4193 - val_categorical_accuracy: 0.8541
Epoch 44/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3022 - categorical_accuracy: 0.8901

979/979 [==============================] - 4s 4ms/step - loss: 0.3022 - categorical_accuracy: 0.8901 - val_loss: 0.3958 - val_categorical_accuracy: 0.8601
Epoch 45/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.3010 - categorical_accuracy: 0.8910

979/979 [==============================] - 4s 4ms/step - loss: 0.3010 - categorical_accuracy: 0.8910 - val_loss: 0.3868 - val_categorical_accuracy: 0.8654
Epoch 46/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2997 - categorical_accuracy: 0.8918

979/979 [==============================] - 4s 4ms/step - loss: 0.2997 - categorical_accuracy: 0.8918 - val_loss: 0.3818 - val_categorical_accuracy: 0.8702
Epoch 47/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2986 - categorical_accuracy: 0.8916

979/979 [==============================] - 4s 4ms/step - loss: 0.2986 - categorical_accuracy: 0.8916 - val_loss: 0.3995 - val_categorical_accuracy: 0.8607
Epoch 48/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2987 - categorical_accuracy: 0.8921

979/979 [==============================] - 4s 4ms/step - loss: 0.2987 - categorical_accuracy: 0.8921 - val_loss: 0.3840 - val_categorical_accuracy: 0.8651
Epoch 49/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2940 - categorical_accuracy: 0.8937 - val_loss: 0.3721 - val_categorical_accuracy: 0.8683
Epoch 50/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2936 - categorical_accuracy: 0.8942 - val_loss: 0.4574 - val_categorical_accuracy: 0.8500
Epoch 51/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2907 - categorical_accuracy: 0.8952 - val_loss: 0.4950 - val_categorical_accuracy: 0.8315
Epoch 52/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2879 - categorical_accuracy: 0.8960

979/979 [==============================] - 4s 4ms/step - loss: 0.2879 - categorical_accuracy: 0.8960 - val_loss: 0.3757 - val_categorical_accuracy: 0.8685
Epoch 53/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2908 - categorical_accuracy: 0.8948 - val_loss: 0.3996 - val_categorical_accuracy: 0.8629
Epoch 54/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2871 - categorical_accuracy: 0.8967 - val_loss: 0.3982 - val_categorical_accuracy: 0.8582
Epoch 55/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2859 - categorical_accuracy: 0.8963

979/979 [==============================] - 4s 4ms/step - loss: 0.2859 - categorical_accuracy: 0.8963 - val_loss: 0.3981 - val_categorical_accuracy: 0.8617
Epoch 56/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2834 - categorical_accuracy: 0.8977

979/979 [==============================] - 4s 4ms/step - loss: 0.2834 - categorical_accuracy: 0.8977 - val_loss: 0.4174 - val_categorical_accuracy: 0.8547
Epoch 57/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2811 - categorical_accuracy: 0.8991 - val_loss: 0.4629 - val_categorical_accuracy: 0.8371
Epoch 58/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2810 - categorical_accuracy: 0.8991 - val_loss: 0.3770 - val_categorical_accuracy: 0.8696
Epoch 59/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2809 - categorical_accuracy: 0.8981

979/979 [==============================] - 4s 4ms/step - loss: 0.2809 - categorical_accuracy: 0.8981 - val_loss: 0.3664 - val_categorical_accuracy: 0.8745
Epoch 60/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2795 - categorical_accuracy: 0.8994 - val_loss: 0.3805 - val_categorical_accuracy: 0.8706
Epoch 61/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2797 - categorical_accuracy: 0.8987 - val_loss: 0.4002 - val_categorical_accuracy: 0.8635
Epoch 62/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2777 - categorical_accuracy: 0.8996

979/979 [==============================] - 4s 4ms/step - loss: 0.2777 - categorical_accuracy: 0.8996 - val_loss: 0.4006 - val_categorical_accuracy: 0.8597
Epoch 63/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2766 - categorical_accuracy: 0.9001

979/979 [==============================] - 4s 4ms/step - loss: 0.2766 - categorical_accuracy: 0.9001 - val_loss: 0.3693 - val_categorical_accuracy: 0.8741
Epoch 64/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2741 - categorical_accuracy: 0.9015 - val_loss: 0.3502 - val_categorical_accuracy: 0.8821
Epoch 65/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2730 - categorical_accuracy: 0.9024 - val_loss: 0.3675 - val_categorical_accuracy: 0.8745
Epoch 66/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2710 - categorical_accuracy: 0.9023

979/979 [==============================] - 4s 4ms/step - loss: 0.2710 - categorical_accuracy: 0.9023 - val_loss: 0.4137 - val_categorical_accuracy: 0.8588
Epoch 67/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2700 - categorical_accuracy: 0.9024

979/979 [==============================] - 4s 4ms/step - loss: 0.2700 - categorical_accuracy: 0.9024 - val_loss: 0.3884 - val_categorical_accuracy: 0.8688
Epoch 68/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2711 - categorical_accuracy: 0.9034 - val_loss: 0.4741 - val_categorical_accuracy: 0.8434
Epoch 69/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2687 - categorical_accuracy: 0.9036

979/979 [==============================] - 4s 4ms/step - loss: 0.2687 - categorical_accuracy: 0.9036 - val_loss: 0.3581 - val_categorical_accuracy: 0.8771
Epoch 70/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2716 - categorical_accuracy: 0.9029

979/979 [==============================] - 4s 4ms/step - loss: 0.2716 - categorical_accuracy: 0.9029 - val_loss: 0.3696 - val_categorical_accuracy: 0.8732
Epoch 71/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2660 - categorical_accuracy: 0.9039

979/979 [==============================] - 4s 4ms/step - loss: 0.2660 - categorical_accuracy: 0.9039 - val_loss: 0.3482 - val_categorical_accuracy: 0.8823
Epoch 72/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2682 - categorical_accuracy: 0.9032 - val_loss: 0.3611 - val_categorical_accuracy: 0.8792
Epoch 73/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2659 - categorical_accuracy: 0.9036 - val_loss: 0.3951 - val_categorical_accuracy: 0.8637
Epoch 74/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2655 - categorical_accuracy: 0.9052

979/979 [==============================] - 4s 4ms/step - loss: 0.2655 - categorical_accuracy: 0.9052 - val_loss: 0.3621 - val_categorical_accuracy: 0.8786
Epoch 75/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2647 - categorical_accuracy: 0.9050 - val_loss: 0.3883 - val_categorical_accuracy: 0.8685
Epoch 76/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2640 - categorical_accuracy: 0.9048 - val_loss: 0.3727 - val_categorical_accuracy: 0.8762
Epoch 77/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2639 - categorical_accuracy: 0.9051

979/979 [==============================] - 4s 4ms/step - loss: 0.2639 - categorical_accuracy: 0.9051 - val_loss: 0.3845 - val_categorical_accuracy: 0.8697
Epoch 78/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2610 - categorical_accuracy: 0.9058

979/979 [==============================] - 4s 4ms/step - loss: 0.2610 - categorical_accuracy: 0.9058 - val_loss: 0.3751 - val_categorical_accuracy: 0.8729
Epoch 79/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2616 - categorical_accuracy: 0.9063 - val_loss: 0.3718 - val_categorical_accuracy: 0.8734
Epoch 80/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2621 - categorical_accuracy: 0.9060 - val_loss: 0.3605 - val_categorical_accuracy: 0.8770
Epoch 81/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2598 - categorical_accuracy: 0.9064

979/979 [==============================] - 4s 4ms/step - loss: 0.2598 - categorical_accuracy: 0.9064 - val_loss: 0.3576 - val_categorical_accuracy: 0.8824
Epoch 82/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2578 - categorical_accuracy: 0.9076

979/979 [==============================] - 4s 4ms/step - loss: 0.2578 - categorical_accuracy: 0.9076 - val_loss: 0.5959 - val_categorical_accuracy: 0.7932
Epoch 83/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2598 - categorical_accuracy: 0.9059 - val_loss: 0.3863 - val_categorical_accuracy: 0.8732
Epoch 84/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2584 - categorical_accuracy: 0.9079

979/979 [==============================] - 4s 4ms/step - loss: 0.2584 - categorical_accuracy: 0.9079 - val_loss: 0.3651 - val_categorical_accuracy: 0.8800
Epoch 85/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2566 - categorical_accuracy: 0.9084

979/979 [==============================] - 4s 4ms/step - loss: 0.2566 - categorical_accuracy: 0.9084 - val_loss: 0.3705 - val_categorical_accuracy: 0.8736
Epoch 86/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2557 - categorical_accuracy: 0.9080

979/979 [==============================] - 4s 4ms/step - loss: 0.2557 - categorical_accuracy: 0.9080 - val_loss: 0.3620 - val_categorical_accuracy: 0.8830
Epoch 87/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2562 - categorical_accuracy: 0.9087 - val_loss: 0.3616 - val_categorical_accuracy: 0.8786
Epoch 88/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2559 - categorical_accuracy: 0.9083

979/979 [==============================] - 4s 4ms/step - loss: 0.2559 - categorical_accuracy: 0.9083 - val_loss: 0.3734 - val_categorical_accuracy: 0.8771
Epoch 89/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2531 - categorical_accuracy: 0.9097

979/979 [==============================] - 4s 4ms/step - loss: 0.2531 - categorical_accuracy: 0.9097 - val_loss: 0.3968 - val_categorical_accuracy: 0.8716
Epoch 90/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2559 - categorical_accuracy: 0.9085 - val_loss: 0.4036 - val_categorical_accuracy: 0.8667
Epoch 91/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2534 - categorical_accuracy: 0.9097 - val_loss: 0.4048 - val_categorical_accuracy: 0.8665
Epoch 92/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2531 - categorical_accuracy: 0.9096

979/979 [==============================] - 4s 4ms/step - loss: 0.2531 - categorical_accuracy: 0.9096 - val_loss: 0.3832 - val_categorical_accuracy: 0.8731
Epoch 93/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2524 - categorical_accuracy: 0.9097 - val_loss: 0.3648 - val_categorical_accuracy: 0.8795
Epoch 94/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2542 - categorical_accuracy: 0.9091 - val_loss: 0.3360 - val_categorical_accuracy: 0.8886
Epoch 95/100

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979/979 [==============================] - 4s 4ms/step - loss: 0.2524 - categorical_accuracy: 0.9093 - val_loss: 0.3768 - val_categorical_accuracy: 0.8776
Epoch 96/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2513 - categorical_accuracy: 0.9104

979/979 [==============================] - 4s 4ms/step - loss: 0.2513 - categorical_accuracy: 0.9104 - val_loss: 0.3651 - val_categorical_accuracy: 0.8795
Epoch 97/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2508 - categorical_accuracy: 0.9106

979/979 [==============================] - 4s 4ms/step - loss: 0.2508 - categorical_accuracy: 0.9106 - val_loss: 0.3857 - val_categorical_accuracy: 0.8725
Epoch 98/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2501 - categorical_accuracy: 0.9106

979/979 [==============================] - 4s 4ms/step - loss: 0.2501 - categorical_accuracy: 0.9106 - val_loss: 0.3540 - val_categorical_accuracy: 0.8825
Epoch 99/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2512 - categorical_accuracy: 0.9105

979/979 [==============================] - 4s 4ms/step - loss: 0.2512 - categorical_accuracy: 0.9105 - val_loss: 0.3629 - val_categorical_accuracy: 0.8802
Epoch 100/100

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979/979 [==============================] - 3s 3ms/step - loss: 0.2492 - categorical_accuracy: 0.9115

979/979 [==============================] - 4s 4ms/step - loss: 0.2492 - categorical_accuracy: 0.9115 - val_loss: 0.3620 - val_categorical_accuracy: 0.8810

#reticulate::py_last_error()

#We can then compute the average of the per-epoch ACC scores for all folds:

eval <- evaluate(model2, test_data, test_targets, verbose = 1)

  1/634 [..............................] - ETA: 1:00 - loss: 0.5886 - categorical_accuracy: 0.7812
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634/634 [==============================] - 1s 2ms/step - loss: 0.8337 - categorical_accuracy: 0.7034

634/634 [==============================] - 1s 2ms/step - loss: 0.8337 - categorical_accuracy: 0.7034